Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation

Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively-researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are typically used to generate or transform synthetic images. Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based approaches. The need for a comprehensive survey of such activity is therefore urgent. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks, including mobile networks, network analysis, internet of things, physical layer, and cybersecurity. In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets.

[1]  Ali Imran,et al.  Generative Adversarial Learning for Machine Learning empowered Self Organizing 5G Networks , 2019, 2019 International Conference on Computing, Networking and Communications (ICNC).

[2]  Gong Zhang,et al.  GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[3]  Yanjiao Chen,et al.  TranGAN: Generative Adversarial Network Based Transfer Learning for Social Tie Prediction , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[4]  Hyunsoo Kim,et al.  Learning to Discover Cross-Domain Relations with Generative Adversarial Networks , 2017, ICML.

[5]  Sudharman K. Jayaweera,et al.  A Survey on Machine-Learning Techniques in Cognitive Radios , 2013, IEEE Communications Surveys & Tutorials.

[6]  Lantao Yu,et al.  SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.

[7]  Jonathon Shlens,et al.  Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.

[8]  Junxing Zhang,et al.  GANSlicing: A GAN-Based Software Defined Mobile Network Slicing Scheme for IoT Applications , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[9]  Jiankun Hu,et al.  A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns , 2014, IEEE Transactions on Computers.

[10]  Brian L. Evans,et al.  Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement , 2017, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.

[11]  Sepp Hochreiter,et al.  GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.

[12]  Babar Shah,et al.  Android malware detection through generative adversarial networks , 2019, Transactions on Emerging Telecommunications Technologies.

[13]  Alexei A. Efros,et al.  Toward Multimodal Image-to-Image Translation , 2017, NIPS.

[14]  Jiannong Cao,et al.  Middleware for Wireless Sensor Networks: A Survey , 2008, Journal of Computer Science and Technology.

[15]  Raymond Y. K. Lau,et al.  Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[16]  Yang Li,et al.  Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities , 2019, IEEE Communications Magazine.

[17]  Weiwei Liu,et al.  An end-to-end generative network for environmental sound-based covert communication , 2018, Multimedia Tools and Applications.

[18]  Seyed Ali Ghorashi,et al.  A novel smartphone application for indoor positioning of users based on machine learning , 2019, UbiComp/ISWC Adjunct.

[19]  Biing-Hwang Juang,et al.  Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).

[20]  Yoshua Bengio,et al.  Boundary-Seeking Generative Adversarial Networks , 2017, ICLR 2017.

[21]  Jian Chen,et al.  Credit Card Fraud Detection Using Sparse Autoencoder and Generative Adversarial Network , 2018, 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON).

[22]  Matthias Bethge,et al.  A note on the evaluation of generative models , 2015, ICLR.

[23]  Jia Shi,et al.  Generative Adversarial Network Assisted Power Allocation for Cooperative Cognitive Covert Communication System , 2020, IEEE Communications Letters.

[24]  Yiannis Demiris,et al.  MAGAN: Margin Adaptation for Generative Adversarial Networks , 2017, ArXiv.

[25]  Bartosz Krawczyk,et al.  Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.

[26]  Timothy J. O'Shea,et al.  Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks , 2018, 2019 International Conference on Computing, Networking and Communications (ICNC).

[27]  Chia-Liang Liu,et al.  Impacts Of I/q Imbalance On Qpsk-ofdm-qam Detection , 1998, International 1998 Conference on Consumer Electronics.

[28]  Andreas Mitschele-Thiel,et al.  Cognitive Cellular Networks: A Q-Learning Framework for Self-Organizing Networks , 2016, IEEE Transactions on Network and Service Management.

[29]  Nan Sun,et al.  WellGAN: Generative-Adversarial-Network-Guided Well Generation for Analog/Mixed-Signal Circuit Layout , 2019, 2019 56th ACM/IEEE Design Automation Conference (DAC).

[30]  Kilian Q. Weinberger,et al.  An empirical study on evaluation metrics of generative adversarial networks , 2018, ArXiv.

[31]  Stephane Villette,et al.  Speech Bandwidth Extension Using Generative Adversarial Networks , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[32]  David Windridge,et al.  Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach , 2020, IEEE Sensors Letters.

[33]  Lorenza Giupponi,et al.  From 4G to 5G: Self-organized Network Management meets Machine Learning , 2017, Comput. Commun..

[34]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[35]  Dusit Niyato,et al.  A Generative Adversarial Learning-Based Approach for Cell Outage Detection in Self-Organizing Cellular Networks , 2020, IEEE Wireless Communications Letters.

[36]  Zhuoning Dong,et al.  Aero-Engine Faults Diagnosis Based on K-Means Improved Wasserstein GAN and Relevant Vector Machine , 2019, 2019 Chinese Control Conference (CCC).

[37]  Timothy J. O'Shea,et al.  Physical Layer Communications System Design Over-the-Air Using Adversarial Networks , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

[38]  David Palacios,et al.  Unsupervised Technique for Automatic Selection of Performance Indicators in Self-Organizing Networks , 2017, IEEE Communications Letters.

[39]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Yimin Zhou,et al.  A Review: Generative Adversarial Networks , 2019, 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[41]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[42]  Roland Vollgraf,et al.  Texture Synthesis with Spatial Generative Adversarial Networks , 2016, ArXiv.

[43]  Changzhen Hu,et al.  An Effective Method to Generate Simulated Attack Data Based on Generative Adversarial Nets , 2018, 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).

[44]  Kemal Davaslioglu,et al.  Generative Adversarial Learning for Spectrum Sensing , 2018, 2018 IEEE International Conference on Communications (ICC).

[45]  Ali Borji,et al.  Pros and Cons of GAN Evaluation Measures , 2018, Comput. Vis. Image Underst..

[46]  Bernhard Schölkopf,et al.  A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..

[47]  David Windridge,et al.  Using GAN to Enhance the Accuracy of Indoor Human Activity Recognition , 2020, ArXiv.

[48]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[49]  Erhan Guven,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2016, IEEE Communications Surveys & Tutorials.

[50]  Richard Demo Souza,et al.  A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks , 2017, IEEE Communications Surveys & Tutorials.

[51]  Yalin E. Sagduyu,et al.  Deep Learning for Launching and Mitigating Wireless Jamming Attacks , 2018, IEEE Transactions on Cognitive Communications and Networking.

[52]  Trevor Darrell,et al.  Adversarial Feature Learning , 2016, ICLR.

[53]  Viktor K. Prasanna,et al.  Generative Adversarial Network for Synthetic Time Series Data Generation in Smart Grids , 2018, 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).

[54]  Purva Raut,et al.  Data Augmentation using Generative models for Credit Card Fraud Detection , 2018, 2018 4th International Conference on Computing Communication and Automation (ICCCA).

[55]  Paul Babyn,et al.  Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..

[56]  Aaron Smith,et al.  A Communication Channel Density Estimating Generative Adversarial Network , 2019, 2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW).

[57]  Stefano Ermon,et al.  Generative Adversarial Imitation Learning , 2016, NIPS.

[58]  Shahrokh Valaee,et al.  A Survey on Behavior Recognition Using WiFi Channel State Information , 2017, IEEE Communications Magazine.

[59]  A. S. Madhukumar,et al.  Spectrum sensing and modulation classification for cognitive radios using cumulants based on fractional lower order statistics , 2013 .

[60]  Hasan Sakir Bilge,et al.  Recent Trends in Deep Generative Models: a Review , 2018, 2018 3rd International Conference on Computer Science and Engineering (UBMK).

[61]  Anil K. Jain,et al.  AdvFaces: Adversarial Face Synthesis , 2019, 2020 IEEE International Joint Conference on Biometrics (IJCB).

[62]  Mounir Ghogho,et al.  Deep learning approach for Network Intrusion Detection in Software Defined Networking , 2016, 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM).

[63]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[64]  Tom White,et al.  Generative Adversarial Networks: An Overview , 2017, IEEE Signal Processing Magazine.

[65]  Ping Tan,et al.  DualGAN: Unsupervised Dual Learning for Image-to-Image Translation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[66]  Yong-Guk Kim,et al.  Android-GAN: Defending against android pattern attacks using multi-modal generative network as anomaly detector , 2020, Expert Syst. Appl..

[67]  Huan Ying,et al.  Power Message Generation in Smart Grid via Generative Adversarial Network , 2019, 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC).

[68]  Mohsen Guizani,et al.  Classification of Small UAVs Based on Auxiliary Classifier Wasserstein GANs , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[69]  Yoav Goldberg,et al.  Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation , 2018, ICML.

[70]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[71]  Kaoru Ota,et al.  Improved MalGAN: Avoiding Malware Detector by Leaning Cleanware Features , 2019, 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).

[72]  Sung-Bae Cho,et al.  Malware Detection Using Deep Transferred Generative Adversarial Networks , 2017, ICONIP.

[73]  Huy Kang Kim,et al.  GIDS: GAN based Intrusion Detection System for In-Vehicle Network , 2018, 2018 16th Annual Conference on Privacy, Security and Trust (PST).

[74]  Yoshua Bengio,et al.  Mode Regularized Generative Adversarial Networks , 2016, ICLR.

[75]  Muhammad Ali Imran,et al.  A Survey of Self Organisation in Future Cellular Networks , 2013, IEEE Communications Surveys & Tutorials.

[76]  Nabin Kumar Karn,et al.  Network Traffic Classification techniques and comparative analysis using Machine Learning algorithms , 2016, 2016 2nd IEEE International Conference on Computer and Communications (ICCC).

[77]  Jun Zhu,et al.  Triple Generative Adversarial Nets , 2017, NIPS.

[78]  Yifan Wu,et al.  Radio Classify Generative Adversarial Networks: A Semi-supervised Method for Modulation Recognition , 2018, 2018 IEEE 18th International Conference on Communication Technology (ICCT).

[79]  Shahram Latifi,et al.  Image Generation with Gans-based Techniques: A Survey , 2019 .

[80]  Shakir Mohamed,et al.  Variational Inference with Normalizing Flows , 2015, ICML.

[81]  Xiaoguo Wang,et al.  A Fraudulent Data Simulation Method Based on Generative Adversarial Networks , 2019 .

[82]  Hamed Haddadi,et al.  Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[83]  Khaled M. Elleithy,et al.  A highly accurate machine learning approach for developing wireless sensor network middleware , 2018, 2018 Wireless Telecommunications Symposium (WTS).

[84]  Tommaso Melodia,et al.  Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey , 2019, Ad Hoc Networks.

[85]  Saikat Guha,et al.  Covert Wireless Communication With Artificial Noise Generation , 2017, IEEE Transactions on Wireless Communications.

[86]  Matthias Schubert,et al.  MMGAN: Generative Adversarial Networks for Multi-Modal Distributions , 2019, ArXiv.

[87]  Qiyue Li,et al.  Wavelet Transform DC-GAN for Diversity Promoted Fingerprint Construction in Indoor Localization , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).

[88]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[89]  Shunming Li,et al.  Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks , 2019, IEEE Access.

[90]  Yuhui Zheng,et al.  Recent Progress on Generative Adversarial Networks (GANs): A Survey , 2019, IEEE Access.

[91]  Simon Haykin,et al.  GradientBased Learning Applied to Document Recognition , 2001 .

[92]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[93]  Zhiguang Qin,et al.  CsiGAN: Robust Channel State Information-Based Activity Recognition With GANs , 2019, IEEE Internet of Things Journal.

[94]  Pan Wang,et al.  FLOWGAN:Unbalanced Network Encrypted Traffic Identification Method Based on GAN , 2019, 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom).

[95]  Tathagata Mukherjee,et al.  Detection of Rogue RF Transmitters using Generative Adversarial Nets , 2019, 2019 IEEE Wireless Communications and Networking Conference (WCNC).

[96]  Laurence T. Yang,et al.  A survey on deep learning for big data , 2018, Inf. Fusion.

[97]  Léon Bottou,et al.  Towards Principled Methods for Training Generative Adversarial Networks , 2017, ICLR.

[98]  Zhi Xue,et al.  IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection , 2018, PAKDD.

[99]  Mohammad Ali Keyvanrad,et al.  A brief survey on deep belief networks and introducing a new object oriented toolbox ( DeeBNet V 3 . 0 ) , 2016 .

[100]  Ananthram Swami,et al.  Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.

[101]  Kemal Davaslioglu,et al.  Generative Adversarial Network for Wireless Signal Spoofing , 2019, WiseML@WiSec.

[102]  Daniel L. Marino,et al.  Generalization of Deep Learning for Cyber-Physical System Security: A Survey , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[103]  Xiaojiang Du,et al.  A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security , 2018, IEEE Communications Surveys & Tutorials.

[104]  Siddique Latif,et al.  Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems , 2019, 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC).

[105]  Jose Ordonez-Lucena,et al.  Network Slicing for 5G with SDN/NFV: Concepts, Architectures, and Challenges , 2017, IEEE Communications Magazine.

[106]  Abhishek Kumar,et al.  Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference , 2017, NIPS.

[107]  Rajen B. Bhatt,et al.  User Localization in an Indoor Environment Using Fuzzy Hybrid of Particle Swarm Optimization & Gravitational Search Algorithm with Neural Networks , 2016, SocProS.

[108]  Kemal Davaslioglu,et al.  Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data , 2018, 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[109]  Jon J. Aho,et al.  Generating Realistic Data for Network Analytics , 2018, MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM).

[110]  Mohammad Eshghi,et al.  A Case Study of Generative Adversarial Networks for Procedural Synthesis of Original Textures in Video Games , 2018, 2018 2nd National and 1st International Digital Games Research Conference: Trends, Technologies, and Applications (DGRC).

[111]  Ala I. Al-Fuqaha,et al.  Path Planning in Support of Smart Mobility Applications Using Generative Adversarial Networks , 2018, 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[112]  Chenglin Wen,et al.  Deep learning fault diagnosis method based on global optimization GAN for unbalanced data , 2020, Knowl. Based Syst..

[113]  Jie Li,et al.  Dynamic Traffic Feature Camouflaging via Generative Adversarial Networks , 2019, 2019 IEEE Conference on Communications and Network Security (CNS).

[114]  Bo Zhang,et al.  Recent Advances of Generative Adversarial Networks in Computer Vision , 2019, IEEE Access.

[115]  Azam Bagheri,et al.  Generative Adversarial Model-Guided Deep Active Learning for Voltage Dip Labelling , 2019, 2019 IEEE Milan PowerTech.

[116]  Kun Xu,et al.  A survey of image synthesis and editing with generative adversarial networks , 2017 .

[117]  Dipankar Raychaudhuri,et al.  Self-Organizing Cellular Radio Access Network with Deep Learning , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[118]  Maria Rigaki,et al.  Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection , 2018, 2018 IEEE Security and Privacy Workshops (SPW).

[119]  Emanuele Ghelfi,et al.  A Survey on GANs for Anomaly Detection , 2019, ArXiv.

[120]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[121]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[122]  Roland Vollgraf,et al.  Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.

[123]  Fei-Yue Wang,et al.  Generative adversarial networks: introduction and outlook , 2017, IEEE/CAA Journal of Automatica Sinica.

[124]  Jakob Hoydis,et al.  An Introduction to Deep Learning for the Physical Layer , 2017, IEEE Transactions on Cognitive Communications and Networking.

[125]  Shiwen Mao,et al.  CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach , 2016, IEEE Transactions on Vehicular Technology.

[126]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[127]  Daniel S. Berman,et al.  A Survey of Deep Learning Methods for Cyber Security , 2019, Inf..

[128]  Muhammad Ali Imran,et al.  Challenges in 5G: how to empower SON with big data for enabling 5G , 2014, IEEE Network.

[129]  Jiann-Shiun Yuan,et al.  Anomaly Generation Using Generative Adversarial Networks in Host-Based Intrusion Detection , 2018, 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[130]  Junliang Wang,et al.  AdaBalGAN: An Improved Generative Adversarial Network With Imbalanced Learning for Wafer Defective Pattern Recognition , 2019, IEEE Transactions on Semiconductor Manufacturing.

[131]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[132]  Jun-Hai Zhai,et al.  Recent Advance On Generative Adversarial Networks , 2018, 2018 International Conference on Machine Learning and Cybernetics (ICMLC).

[133]  Chen Wang,et al.  Wireless Sensing for Human Activity: A Survey , 2020, IEEE Communications Surveys & Tutorials.

[134]  David Pfau,et al.  Connecting Generative Adversarial Networks and Actor-Critic Methods , 2016, ArXiv.

[135]  Hongyu Chen,et al.  Generating Music Algorithm with Deep Convolutional Generative Adversarial Networks , 2019, 2019 IEEE 2nd International Conference on Electronics Technology (ICET).

[136]  Ying Tan,et al.  Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN , 2017, DMBD.

[137]  Sergey Levine,et al.  A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models , 2016, ArXiv.

[138]  Fernando Pérez-Cruz,et al.  PassGAN: A Deep Learning Approach for Password Guessing , 2017, ACNS.

[139]  Tao Zhang,et al.  A Transfer Learning Strategy for Rotation Machinery Fault Diagnosis based on Cycle-Consistent Generative Adversarial Networks , 2018, 2018 Chinese Automation Congress (CAC).

[140]  Timothy J. O'Shea,et al.  Applications of Machine Learning to Cognitive Radio Networks , 2007, IEEE Wireless Communications.